Editing IPCC:AR6/SRCCL/Chapter-7 (section)

== CCB12 Traditional biomass use: Land, climate and development implications ==

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Francis X. Johnson (Sweden), Fahmuddin Agus (Indonesia), Rob Bailis (The United States of America), Suruchi Bhadwal (India), Annette Cowie (Australia), Tek Sapkota (Nepal)

'''Introduction and significance'''

Most biomass used for energy today is in traditional forms (fuelwood, charcoal, agricultural residues) for cooking and heating by some 3 billion people worldwide (IEA 2017). Traditional biomass has high land and climate impacts, with significant harvesting losses, greenhouse gas (GHG) emissions, soil impacts and high conversion losses (Cutz et al. 2017b; Masera et al. 2015; Ghilardi et al. 2016a; Bailis et al. 2015; Fritsche et al. 2017b; Mudombi et al. 2018b). In addition to these impacts, indoor air pollution from household cooking is a leading cause of mortality in low- and medium-income countries and especially affects women and children (Smith et al. 2014a; HEI/IHME 2018; Goldemberg et al. 2018b). In rural areas, the significant time needed for gathering fuelwood imposes further costs on women and children (Njenga and Mendum 2018; Gurung and Oh 2013a; Behera et al. 2015a).

Both agricultural and woody biomass can be upgraded and used sustainably through improved resource management and modern conversion technologies, providing much greater energy output per unit of biomass (Cutz et al. 2017b; Hoffmann et al. 2015a; Gurung and Oh 2013b). More relevant than technical efficiency is the improved quality of energy services: with increasing income levels and/or access to technologies, households transition over time from agricultural residues and fuelwood to charcoal and then to gaseous or liquid fuels and electricity (Leach 1992; Pachauri and Jiang 2008; Goldemberg and Teixeira Coelho 2004; Smeets et al. 2012a). However, most households use multiple stoves and/or fuels at the same time, known as ‘fuel stacking’ for economic flexibility and also for socio-cultural reasons (Ruiz-Mercado and Masera 2015a; Cheng and Urpelainen 2014; Takama et al. 2012).

'''Urban and rural use of traditional biomass'''

In rural areas, fuelwood is often gathered at no cost to the user, and burned directly whereas, in urban areas, traditional biomass use may often involve semi-processed fuels, particularly in Sub-Saharan Africa where charcoal is the primary urban cooking fuel. Rapid urbanisation and/or commercialisation drives a shift from fuelwood to charcoal, which results in significantly higher wood use ( ''very high confidence'' ) due to losses in charcoal supply chains and the tendency to use whole trees for charcoal production (Santos et al. 2017; World Bank. 2009a; Hojas-Gascon et al. 2016a; Smeets et al. 2012b). One study in Myanmar found that charcoal required 23 times the land area of fuelwood (Win et al. 2018). In areas of woody biomass scarcity, animal dung and agricultural residues, as well as lower-quality wood, are often used (Kumar Nath et al. 2013a; Go et al. 2019a; Jagger and Kittner 2017; Behera et al. 2015b). The fraction of woody biomass harvested that is not ‘demonstrably renewable’ is the fraction of non-renewable biomass (fNRB) under UNFCCC accounting; default values for fNRB for least-developed countries and small island developing states ranged from 40–100% (CDM Executive Board 2012). Uncertainties in woodfuel data, complexities in spatiotemporal woodfuel modelling and rapid forest regrowth in some tropical regions present sources of variation in such estimates, and some fNRB values are likely to have been overestimated (McNicol et al. 2018a; Ghilardi et al. 2016b; Bailis et al. 2017b).

'''GHG emissions and traditional biomass'''

Due to over-harvesting, incomplete combustion and the effects of short-lived climate pollutants, traditional woodfuels (fuelwood and charcoal) contribute 1.9–2.3% of global GHG emissions; non-renewable biomass is concentrated especially in ‘hotspot’ regions of East Africa and South Asia (Bailis et al. 2015). The estimate only includes woody biomass and does not account for possible losses in soil carbon or the effects of nutrient losses from use of animal dung, which can be significant in some cases (Duguma et al. 2014a; Achat et al. 2015a; Sánchez et al. 2016). Reducing emissions of black carbon alongside GHG reductions offers immediate health co-benefits (Shindell et al. 2012; Pandey et al. 2017; Weyant et al. 2019a; Sparrevik et al. 2015). Significant GHG emissions reductions, depending on baseline or reference use, can be obtained through fuel-switching to gaseous and liquid fuels, sustainable harvesting of woodfuels, upgrading to efficient stoves, and adopting high-quality processed fuels such as wood pellets ( ''medium evidence, high agreement'' ) (Wathore et al. 2017; Jagger and Das 2018; Quinn et al. 2018; Cutz et al. 2017b; Carter et al. 2018; Bailis et al. 2015; Ghilardi et al. 2018; Weyant et al. 2019b; Hoffmann et al. 2015b).

'''Land and forest degradation'''

Land degradation is itself a significant source of GHG emissions and biodiversity loss, with over-harvesting of woodfuel as a major cause in some regions and especially in Sub-Saharan Africa (Pearson et al. 2017; Joana Specht et al. 2015a; Kiruki et al. 2017b; Ndegwa et al. 2016; McNicol et al. 2018b). Reliance on traditional biomass is quite land-intensive: supplying one household sustainably for a year can require more than half a hectare of land, which, in dryland countries such as Kenya, can result in substantial percentage of total tree cover (Fuso Nerini et al. 2017). In Sub-Saharan Africa and in some other regions, land degradation is widely associated with charcoal production ( ''high confidence'' ), often in combination with timber harvesting or clearing land for agriculture (Kiruki et al. 2017a; Ndegwa et al. 2016; Hojas-Gascon et al. 2016b). Yet charcoal makes a significant contribution to livelihoods in many areas and thus, in spite of the ecological damage, halting charcoal production is difficult due to the lack of alternative livelihoods and/or the affordability of other fuels (Smith et al. 2015; Zulu and Richardson 2013a; Jones et al. 2016a; World Bank 2009b).

'''Use of agricultural residues and animal dung for bioenergy'''

Although agricultural wastes and residues from almost any crop can be used in many cases for bioenergy, excessive removal or reduction of forest (or agricultural) biomass can contribute to a loss of soil carbon, which can also, in turn, contribute to land degradation (James et al. 2016; Blanco-Canqui and Lal 2009a; Carvalho et al. 2016; Achat et al. 2015b; Stavi and Lal 2015). Removals are limited to levels at which problems of soil erosion, depletion of soil organic matter, soil nutrient depletion and decline in crop yield are effectively mitigated (Ayamga et al. 2015a; Baudron et al. 2014; Blanco-Canqui and Lal 2009b). Application or recycling of residues may, in some cases, be more valuable for soil improvement ( ''medium confidence'' ). Tao et al. (2017) used leftover oil palm fruit bunches and demonstrated that application of 30 to 90 t ha–1 empty fruit bunches maintains high palm oil yield with low temporal variability. A wide variety of wastes from palm oil harvesting can be used for bioenergy, including annual crop residues (Go et al. 2019b; Ayamga et al. 2015b; Gardner et al. 2018b).

Animal dung is a low-quality fuel used where woody biomass is scarce, such as in South Asia and some areas of eastern Africa (Duguma et al. 2014b; Behera et al. 2015b; Kumar Nath et al. 2013b). Carbon and nutrient losses can be significant when animal dung is dried and burned as cake, whereas using dung in a biodigester provides high-quality fuel and preserves nutrients in the by-product slurry (Clemens et al. 2018; Gurung and Oh 2013b; Quinn et al. 2018).

'''Production and use of biochar'''

Converting agricultural residues into biochar can also help to reverse trends of soil degradation (Section 4.10.7). The positive effects of using biochar have been demonstrated in terms of soil aggregate improvement, increase of exchangeable cations, cation exchange capacity, available phosphorus, soil pH and carbon sequestration as well as increased crop yields (Huang et al. 2018; El-Naggar et al. 2018; Wang et al. 2018; Oladele et al. 2019; Blanco-Canqui and Lal 2009b). The level of biochar effectiveness varies depending on the kind of feedstock, soil properties and rate of application (Shaaban et al. 2018; Pokharel and Chang 2019). In addition to adding value to an energy product, the use of biochar offers a climate-smart approach to addressing agricultural productivity (Solomon and Lehmann 2017).

'''Relationship to food security and other Sustainable Development Goals (SDGs)'''

The population that is food insecure also intersects significantly with those relying heavily on traditional biomass such that poor and vulnerable populations often expend considerable time (gathering fuel) or use a significant share of household income for low-quality energy services (Fuso Nerini et al. 2017; McCollum et al. 2018; Rao and Pachauri 2017; Pachauri et al. 2018; Muller and Yan 2018; Takama et al. 2012). Improvements in energy access and reduction or elimination of traditional biomass use thus have benefits across multiple SDGs ( ''medium evidence, high agreement'' ) (Masera et al. 2015; Rao and Pachauri 2017; Pachauri et al. 2018; Hoffmann et al. 2017; Jeuland et al. 2015; Takama et al. 2012; Gitau et al. 2019; Quinn et al. 2018; Ruiz-Mercado and Masera 2015b; Duguma et al. 2014b; Sola et al. 2016b). Improved energy access contributes to adaptive capacity, although charcoal production itself can also serve as a diversification or adaptation strategy (Perera et al. 2015; Ochieng et al. 2014; Sumiya 2016; Suckall et al. 2015; Jones et al. 2016b).

'''Socio-economic choices and shifts'''

When confronted with the limitations of higher-priced household energy alternatives, climate mitigation policies can result in trade- offs with health, energy access and other SDGs (Cameron et al. 2016; Fuso Nerini et al. 2018). The poorest households have no margin to pay for higher-cost efficient stoves; a focus on product-specific characteristics, user needs and/or making clean options more available would improve the market take-up ( ''medium confidence'' ) (Takama et al. 2012; Mudombi et al. 2018c; Khandelwal et al. 2017; Rosenthal et al. 2017; Cundale et al. 2017; Jürisoo et al. 2018). Subsidies for more efficient end-use technologies, in combination with promotion of sustainable harvesting techniques, would provide the highest emissions reductions while improving energy services (Cutz et al. 2017a).

'''Knowledge gaps'''

Unlike analyses on modern energy sources, scientific assessments on traditional biomass use are complicated by its informal nature and the difficulty of tracing data and impacts; more systematic analytical efforts are needed to address this research gap  Cerutti et al. 2015). In general, traditional biomass use is associated with poverty. Therefore, efforts to reduce the dependence on fuelwood use are to be conducted in coherence with poverty alleviation (McCollum et al. 2018; Joana Specht et al. 2015b; Zulu and Richardson 2013b). The substantial potential co-benefits suggest that the traditional biomass sector remains under- researched and under-exploited in terms of cost-effective emissions reductions, as well as for synergies between climate stabilisation goals and other SDGs.

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=== 7.6.3 Adaptive climate governance responding to uncertainty ===

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In the 1990s, adaptive governance emerged from adaptive management (Holling 1978, 1986), combining resilience and complexity theory, and reflecting the trend of moving from government to governance (Hurlbert 2018b <sup>[[#fn:r1336|1336]]</sup> ). Adaptive governance builds on multi-level and polycentric governance. Adaptive governance is ‘a process of resolving trade-offs and charting a course for sustainability’ (Boyle et al. 2001, p. 28) through a range of ‘political, social, economic and administrative systems that develop, manage and distribute a resource in a manner promoting resilience through collaborative, flexible and learning-based issue management across different scales’ (Hurlbert 2018,p.25). There is ''medium evidence'' and ''medium agreement'' that few alternative governance theories handle processes of change characterised by nonlinear dynamics, threshold effects, cascades and limited predictability; however, the majority of literature relates to the USA or Canada (Karpouzoglou et al. 2016 <sup>[[#fn:r1337|1337]]</sup> ). Combining adaptive governance with other theories has allowed good evaluation of important governance features such as power and politics, inclusion and equity, short-term and long-term change, and the relationship between public policy and adaptive governance (Karpouzoglou et al. 2016).

There is ''robust evidence'' and ''high agreement'' that resource and disaster crises are crises of governance (Pahl-Wostl 2017b <sup>[[#fn:r1338|1338]]</sup> ; Villagra and Quintana 2017 <sup>[[#fn:r1339|1339]]</sup> ; Gupta et al. 2013b <sup>[[#fn:r1340|1340]]</sup> ). Adaptive governance of risk has emerged in response to these crises and involves four critical pillars (Fra.Paleo 2015 <sup>[[#fn:r1341|1341]]</sup> ):

# Sustainability as a response to environmental degradation, resource depletion and ES deterioration
# Recognition that governance is required as government is unable to resolve key societal and environmental problems, including climate change and complex problems
# Mitigation as a means to reduce vulnerability and avoid exposure
# Adaptation responds to changes in environmental conditions.

Closely related to (and arguably components of) adaptive governance are adaptive management (Section 7.5.4) (a regulatory environment that manages ecological system boundaries through hypothesis testing, monitoring, and re-evaluation (Mostert et al. 2007 <sup>[[#fn:r1342|1342]]</sup> )), adaptive co-management (flexible community-based resource management (Plummer and Baird 2013 <sup>[[#fn:r1343|1343]]</sup> )), and anticipatory governance (flexible decision-making through the use of scenario planning and reiterative policy review (Boyd et al. 2015 <sup>[[#fn:r1344|1344]]</sup> )). Adaptive governance can be conceptualised as including multilevel governance with a balance

between top-down and bottom-up decision-making that is performed by many actors (including citizens) in both formal and informal networks, allowing policy measures and governance arrangements to be tailored to local context and matched at the appropriate scale of the problem, allowing for opportunities for experimentation and learning by individuals and social groups (Rouillard et al. 2013 <sup>[[#fn:r1345|1345]]</sup> ; Hurlbert 2018b <sup>[[#fn:r1346|1346]]</sup> ).

There is ''high confidence'' that anticipation is a key component of adaptive climate governance wherein steering mechanisms in the present are developed to adapt to and/or shape uncertain futures (Vervoort and Gupta 2018 <sup>[[#fn:r1347|1347]]</sup> ; Wiebe et al. 2018 <sup>[[#fn:r1348|1348]]</sup> ; Fuerth 2009 <sup>[[#fn:r1349|1349]]</sup> ). Effecting this anticipatory governance involves simultaneously making short-term decisions in the context of longer-term policy visioning, anticipating future climate change models and scenarios in order to realise a more sustainable future (Bates and Saint-Pierre 2018 <sup>[[#fn:r1350|1350]]</sup> ; Serrao-Neumann et al. 2013 <sup>[[#fn:r1351|1351]]</sup> ; Boyd et al. 2015 <sup>[[#fn:r1352|1352]]</sup> ). Utilising the decision- making tools and practices in Section 7.5, policymakers operationalise anticipatory governance through a foresight system considering future scenarios and models, a networked system for integrating this knowledge into the policy process, a feedback system using indicators (Section 7.5.5) to gauge performance, an open-minded institutional culture allowing for hybrid and polycentric governance (Fuerth and Faber 2013 <sup>[[#fn:r1353|1353]]</sup> ; Fuerth 2009 <sup>[[#fn:r1354|1354]]</sup> ).

There is ''high confidence'' that, in order to manage uncertainty, natural resource governance systems need to allow agencies and stakeholders to learn and change over time, responding to ecosystem changes and new information with different management strategies and practices that involve experimentation (Camacho 2009 <sup>[[#fn:r1355|1355]]</sup> ; Young 2017b <sup>[[#fn:r1356|1356]]</sup> ).Thereis emerging literature on experimentation in governance surrounding climate change and land use (Kivimaa et al. 2017a <sup>[[#fn:r1357|1357]]</sup> ) including policies such as REDD+ (Kaisa et al. 2017 <sup>[[#fn:r1358|1358]]</sup> ). Governance experiment literature could be in relation to scaling up policies from the local level for greater application, or downscaling policies addressing broad complex issues such as climate change, or addressing necessary change in social processes across sectors (such as water energy and food) (Laakso et al. 2017 <sup>[[#fn:r1359|1359]]</sup> ). Successful development of new policy instruments occurred in a governance experiment relating to coastal policy adapting to rising sea levels and extreme weather events through planned retreat (Rocle and Salles 2018 <sup>[[#fn:r1360|1360]]</sup> ). Experiments in emissions trading between 1968 and 2000 in the USA helped to realise specific models of governance and material practices through mutually supportive lab experiments and field applications that advanced collective knowledge (Voß and Simons 2018 <sup>[[#fn:r1361|1361]]</sup> ).

There is ''high confidence'' that an SLM plan is dynamic and adaptive over time to (unforeseen) future conditions by monitoring indicators as early warnings or signals of tipping points, initiating a process of change in policy pathway before a harmful threshold is reached (Stephens et al. 2018, 2017; Haasnoot et al. 2013 <sup>[[#fn:r1362|1362]]</sup> ; Bloemen et al. 2018 <sup>[[#fn:r1363|1363]]</sup> ) (Section 7.5.2.2). This process has been applied in relation to coastal sea level rise, starting with low-risk, low-cost measures and working up to measures requiring greater investment after review and reevaluation (Barnett et al. 2014 <sup>[[#fn:r1364|1364]]</sup> ). A first measure was stringent controls of new development, graduating to managed relocation of low-lying critical infrastructure, and eventually movement of habitable dwellings to more elevated parts of town, as flooding and inundation triggers are experienced (Haasnoot et al. 2018 <sup>[[#fn:r1365|1365]]</sup> ; Lawrence et al. 2018 <sup>[[#fn:r1366|1366]]</sup> ; Barnett et al. 2014 <sup>[[#fn:r1367|1367]]</sup> ; Stephens et al. 2018 <sup>[[#fn:r1368|1368]]</sup> ). Nanda et al. (2018) <sup>[[#fn:r1369|1369]]</sup> apply the concept to a wetland in Australia to identify a mix of short- and long-term decisions, and Prober et al. (2017) <sup>[[#fn:r1371|1371]]</sup> develop adaptation pathways for agricultural landscapes, also in Australia. Both studies identify that longer-term decisions may involve a considerable change to institutional arrangements at different scales. Viewing climate mitigation as a series of connected decisions over a long time period and not an isolated decision, reduces the fragmentation and uncertainty endemic of models and effectiveness of policy measures (Roelich and Giesekam 2019 <sup>[[#fn:r1372|1372]]</sup> ).

There is ''medium evidence'' and ''high agreement'' that participatory processes in adaptive governance within and across policy regimes overcome limitations of polycentric governance, allowing priorities to be set in sustainable development through rural land management and integrated water resource management (Rouillard et al. 2013 <sup>[[#fn:r1373|1373]]</sup> ).Adaptive governance addresses large uncertainties and their social amplification through differing perceptions of risk (Kasperson 2012 <sup>[[#fn:r1374|1374]]</sup> ; Fra.Paleo 2015 <sup>[[#fn:r1375|1375]]</sup> ) offering an approach to co-evolve with risk by implementing policy mixes and assessing effectiveness in an ongoing process, making mid-point corrections when necessary (Fra.Paleo 2015). In respect of climate adaptation to coastal and riverine land erosion due to extreme weather events impacting on communities, adaptive governance offers the capacity to monitor local socio-economic processes and implement dynamic locally informed institutional responses. In Alaska, adaptive governance responded to the dynamic risk of extreme weather events and issue of climate migration by providing a continuum of policy from protection in place to community relocation, integrating across levels and actors in a more effective and less costly response option than other governance systems (Bronen and Chapin 2013 <sup>[[#fn:r1376|1376]]</sup> ). In comparison to other governance initiatives of ecosystem management aimed at conservation and sustainable use of natural capital, adaptive governance has visible effects on natural capital by monitoring, communicating and responding to ecosystem-wide changes at the landscape level (Schultz et al. 2015 <sup>[[#fn:r1377|1377]]</sup> ). Adaptive governance can be applied to manage drought assistance as a common property resource. Adaptive governance can manage complex, interacting goals to create innovative policy options, facilitated through nested and polycentric systems of governance, effected by watershed or catchment management groups in areas of natural resource management (Nelson et al. 2008 <sup>[[#fn:r1378|1378]]</sup> ).

There is ''medium evidence'' and ''high agreement'' that transformational change is a necessary societal response option to manage climate risks which is uniquely characterised by the depth of change needed to reframe problems and change dominant mindsets, the scope of change needed (that is larger than just a few people) and the speed of change required to reduce emissions (O’Brien et al. 2012 <sup>[[#fn:r1379|1379]]</sup> ; Termeer et al. 2017 <sup>[[#fn:r1380|1380]]</sup> ). Transformation of governance occurs with changes in values to reflect an understanding that the environmental crisis occurs in the context of our relation with the earth (Hordijk et al. 2014 <sup>[[#fn:r1381|1381]]</sup> ; Pelling 2010 <sup>[[#fn:r1382|1382]]</sup> ). Transformation can happen by intervention strategies that enable small in-depth wins, amplify these small wins through integration into existing practices, and unblock stagnations (locked in structures) preventing transformation by confronting social and cognitive fixations with counterintuitive interventions (Termeer et al. 2017 <sup>[[#fn:r1383|1383]]</sup> ). Iterative consideration of issues and reformulation of policy instruments and response options facilitates transformation by allowing experimentation (Monkelbaan 2019 <sup>[[#fn:r1384|1384]]</sup> ).

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